26. Determinants I. 1. Prehistory

Transcription

1 26. Determinants I 26.1 Prehistory 26.2 Definitions 26.3 Uniqueness and other properties 26.4 Existence Both as a careful review of a more pedestrian viewpoint, and as a transition to a coordinate-independent approach, we roughly follow Emil Artin s rigorization of erminants of matrices with entries in a field. Standard properties are derived, in particular uniqueness, from simple assumptions. We also prove existence. Soon, however, we will want to develop corresponding intrinsic versions of ideas about endomorphisms. This is multilinear algebra. Further, for example to treat the Cayley-Hamilton theorem in a forthright manner, we will want to consider modules over commutative rings, not merely vector spaces over fields. 1. Prehistory Determinants arose many years ago in formulas for solving linear equations. This is Cramer s Rule, described as follows. [1] Consider a system of n linear equations in n unknowns x 1,..., x n a 11 x 1 + a 12 x a 1n x n = c 1 a 21 x 1 + a 22 x a 2n x n = c 2 a 31 x 1 + a 32 x a 3n x n = c a n1 x 1 + a n2 x a nn x n = c n [1] We will prove Cramer s Rule just a little later. In fact, quite contrary to a naive intuition, the proof is very easy from an only slightly more sophisticated viewpoint. 379

2 380 Determinants I Let A be the matrix with (i, j) th entry a ij. Let A (l) be the matrix A with its l th column replaced by the c i s, that is, the (i, l) th entry of A (l) is c l. Then Cramer s Rule asserts that x l = A(l) A where is erminant, at least for A 0. It is implicit that the coefficients a ij and the constants c l are in a field. As a practical method for solving linear systems Cramer s Rule is far from optimal. Gaussian elimination is much more efficient, but is less interesting. Ironically, in the context of very elementary mathematics it seems difficult to give an intelligible definition or formula for erminants of arbitrary sizes, so typical discussions are limited to very small matrices. For example, in the 2-by-2 case there is the palatable formula a b = ad bc c d Thus, for the linear system by Cramer s Rule ax + by = c 1 cx + dy = c 2 c1 b a c1 c 2 d c c 2 x = y = a b a b c d c d In the 3-by-3 case there is the still-barely-tractable formula (reachable by a variety of elementary mnemonics) a 11 a 12 a 13 a 21 a 22 a 23 a 31 a 32 a 33 = (a 11 a 22 a 33 + a 12 a 23 a 31 + a 13 a 21 a 32 ) (a 31 a 22 a 13 + a 32 a 23 a 11 + a 31 a 21 a 12 ) Larger erminants are defined ambiguously by induction as expansions by minors. [2] Inverses of matrices are expressible, inefficiently, in terms of erminants. adjugate matrix A adjg of an n-by-n matrix A has (i, j) th entry The cofactor matrix or A adjg ij = ( 1) i+j A (ji) where A ji is the matrix A with j th row and i th column removed. [3] Then A A adjg = ( A) 1 n where 1 n is the n-by-n identity matrix. That is, if A is invertible, A 1 = 1 A Aadjg [2] We describe expansion by minors just a little later, and prove that it is in fact unambiguous and correct. [3] Yes, there is a reversal of indices: the (ij) th entry of A adjg is, up to sign, the erminant of A with j th row and i th column removed. Later discussion of exterior algebra will clarify this construction/formula.

3 In the 2-by-2 case this formula is useful: Garrett: Abstract Algebra a b 1 d b = c d ad bc c a Similarly, a matrix (with entries in a field) is invertible if and only if its erminant is non-zero. [4] The Cayley-Hamilton theorem is a widely misunderstood result, often given with seriously flawed proofs. [5] The characteristic polynomial P T (x) of an n-by-n matrix T is defined to be The assertion is that P T (x) = (x 1 n T ) P T (T ) = 0 n where 0 n is the n-by-n zero matrix. The main use of this is that the eigenvalues of T are the roots of P T (x) = 0. However, except for very small matrices, this is a suboptimal computational approach, and the minimal polynomial is far more useful for demonstrating qualitative facts about endomorphisms. Nevertheless, because there is a formula for the characteristic polynomial, it has a substantial popularity. The easiest false proof of the Cayley-Hamilton Theorem is to apparently compute P T (T ) = (T 1 n T ) = (T T ) = (0 n ) = 0 The problem is that the substitution x 1 n T 1 n is not legitimate. The operation cannot be any kind of scalar multiplication after T is substituted for x, nor can it be composition of endomorphisms (nor multiplication of matrices). Further, there are interesting fallacious explanations of this incorrectness. For example, to say that we cannot substitute the non-scalar T for the scalar variable x fails to recognize that this is exactly what happens in the assertion of the theorem, and fails to see that the real problem is in the notion of the scalar multiplication of 1 n by x. That is, the correct objection is that x 1 n is no longer a matrix with entries in the original field k (whatever that was), but in the polynomial ring k[x], or in its field of fractions k(x). But then it is much less clear what it might mean to substitute T for x, if x has become a kind of scalar. Indeed, Cayley and Hamilton only proved the result in the 2-by-2 and 3-by-3 cases, by direct computation. Often a correct argument is given that invokes the (existence part of the) structure theorem for finitelygenerated modules over PIDs. A little later, our discussion of exterior algebra will allow a more direct argument, using the adjugate matrix. More importantly, the exterior algebra will make possible the longpostponed uniqueness part of the proof of the structure theorem for finitely-generated modules over PIDs. [4] We prove this later in a much broader context. [5] We give two different correct proofs later.

4 382 Determinants I 2. Definitions For the present discussion, a erminant is a function D of square matrices with entries in a field k, taking values in that field, satisfying the following properties. Linearity as a function of each column: letting C 1,..., C n in k n be the columns of an n-by-n matrix C, for each 1 i n the function C i D(C) is a k-linear map k n k. [6] That is, for scalar b and for two columns C i and C i D(..., bc i,...) = b D(..., C i,...) D(..., C i + C i,...) = D(..., C i,...) + D(..., C i,...) Alternating property: [7] If two adjacent columns of a matrix are equal, the erminant is 0. Normalization: The erminant of an identity matrix is 1: D = That is, as a function of the columns, if the columns are the standard basis vectors in k n then the value of the erminant is Uniqueness and other properties If two columns of a matrix are interchanged the value of the erminant is multiplied by 1. That is, writing the erminant as a function of the columns we have D(C) = D(C 1,..., C n ) D(C 1,..., C i 1, C i, C i+1, C i+2,..., C n ) = D(C 1,..., C i 1, C i+1, C i, C i+2,..., C n ) Proof: There is a little trick here. Consider the matrix with C i + C j at both the i th and j th columns. Using the linearity in both i th and j th columns, we have 0 = D(..., C i + C j,..., C i + C j,...) [6] Linearity as a function of several vector arguments is called multilinearity. [7] The etymology of alternating is somewhat obscure, but does have a broader related usage, referring to rings that are anti-commutative, that is, in which x y = y x. We will see how this is related to the present situation when we talk about exterior algebras. Another important family of alternating rings is Lie algebras, named after Sophus Lie, but in these the product is written [x, y] rather than x y, both by convention and for functional reasons.

5 Garrett: Abstract Algebra 383 = D(..., C i,..., C i,...) + D(..., C i,..., C j,...) + D(..., C j,..., C i,...) + D(..., C j,..., C j,...) The first and last erminants on the right are also 0, since the matrices have two identical columns. Thus, 0 = D(..., C i,..., C j,...) + D(..., C j,..., C i,...) as claimed. /// [3.0.1] Remark: If the characteristic of the underlying field k is not 2, then we can replace the requirement that equality of two columns forces a erminant to be 0 by the requirement that interchange of two columns multiplies the erminant by 1. But this latter is a strictly weaker condition when the characteristic is 2. For any permutation π of {1, 2, 3,..., n} we have D(C π(1),..., C π(n) ) = σ(π) D(C 1,..., C n ) where C i are the columns of a square matrix and σ is the sign function on S n. Proof: This argument is completely natural. The adjacent transpositions generate the permutation group S n, and the sign function σ(π) evaluated on a permutation π is ( 1) t where t is the number of adjacent transpositions used to express π in terms of adjacent permutations. /// The value of a erminant is unchanged if a multiple of one column is added to another. That is, for indices i < j, with columns C i considered as vectors in k n, and for b k, D(..., C i,..., C j,...) = D(..., C i,..., C j + bc i,...) D(..., C i,..., C j,...) = D(..., C i + bc j,..., C j,...) Proof: Using the linearity in the j th column, D(..., C i,..., C j + bc i,...) = D(..., C i,..., C j,...) + b D(..., C i,..., C i,...) = D(..., C i,..., C j,...) + b 0 = D(..., C i,..., C j,...) since a erminant is 0 if two columns are equal. /// Let C j = i b ij A i where b ij are in k and A i k n. Let C be the matrix with i th column C i, and let A the the matrix with i th column A i. Then D(C) = σ(π) b π(1),1..., b π(n),n D(A) and also D(C) = ( σ(π) b 1,π(1),1..., b n,π(n) ) D(A)

6 384 Determinants I Proof: First, expanding using (multi-) linearity, we have D(..., C j,...) = D(..., i b ija i,...) = b i1,1... b in,n D(A i1,..., A in ) i 1,...,i n where the ordered n-tuple i 1,..., i n is summed over all choices of ordered n-tupes with entries from {1,..., n}. If any two of i p and i q with p q are equal, then the matrix formed from the A i will have two identical columns, and will be 0. Thus, we may as well sum over permutations of the ordered n-tuple 1, 2, 3,..., n. Letting π be the permutation which takes l to i l, we have D(A i1,..., A in ) = σ(π) D(A 1,..., A n ) Thus, D(C) = D(..., C j,...) = ( σ(π) b π(1),1..., b n,π(n) ) D(A) as claimed. For the second, complementary, formula, since multiplication in k is commutative, Also, b π(1),1... b π(n),n = b 1,π 1 (1)... b n,π 1 (n) 1 = σ(1) = σ(π π 1 ) = σ(π) σ(π 1 ) And the map π π 1 is a bijecton of S n to itself, so σ(π) b π(1),1..., b n,π(n) = σ(π) b 1,π(1)..., b π(n),n which yields the second formula. /// [3.0.2] Remark: So far we have not used the normalization that the erminant of the identity matrix is 1. Now we will use this. Let c ij be the (i, j) th entry of an n-by-n matrix C. Then D(C) = σ(π) c π(1),1..., c n,π(n) Proof: In the previous result, take A to be the identity matrix. /// (Uniqueness) There is at most one one erminant function on n-by-n matrices. Proof: The previous formula is valid once we prove that erminants exist. /// The transpose C of C has the same erminant as does C D(C ) = D(C) Proof: Let c ij be the (i, j) th entry of C. The (i, j) th entry c ij of C is c ji, and we have shown that D(C ) = σ(π) c π(1),1... c π(n),n

7 Garrett: Abstract Algebra 385 Thus, which is also D(C), as just shown. D(C ) = σ(π) c π(1),1... c n,π(n) /// (Multiplicativity) For two square matrices A, B with entries a ij and b ij and product C = AB with entries c ij, we have D(AB) = D(A) D(B) Proof: The j th column C j of the product C is the linear combination A 1 b 1,j A n b n,j of the columns A 1,..., A n of A. Thus, from above, D(AB) = D(C) = σ(π) b π(1),1... b π(n),1 D(A) π And we know that the sum is D(B). /// If two rows of a matrix are identical, then its erminant is 0. Proof: Taking transpose leaves the erminant alone, and a matrix with two identical columns has erminant 0. /// Cramer s Rule Let A be an n-by-n matrix with j th column A j. Let b be a column vector with i th entry b i. Let x be a column vector with i th entry x i. Let A (l) be the matrix obtained from A by replacing the j th column A j by b. Then a solution x to an equation Ax = b is given by if D(A) 0. x l = D(A(l) ) D(A) Proof: This follows directly from the alternating multilinear nature of erminants. First, the equation Ax = b can be rewritten as an expression of b as a linear combination of the columns of A, namely b = x 1 A 1 + x 2 A x n A n Then D(A (l) ) = D(..., A l 1, j x j A j, A l+1,...) = j x j D(..., A l 1, A j, A l+1,...) = x l D(..., A l 1, A l, A l+1,...) = x l D(A) since the erminant is 0 whenever two columns are identical, that is, unless l = j. /// [3.0.3] Remark: In fact, this proof of Cramer s Rule does a little more than verify the formula. First, even if D(A) = 0, still D(A (l) ) = x l D(A)

8 386 Determinants I Second, for D(A) 0, the computation actually shows that the solution x is unique (since any solutions x l s satisfy the indicated relation). An n-by-n matrix is invertible if and only if its erminant is non-zero. Proof: If A has an inverse A 1, then from A A 1 = 1 n and the multiplicativity of erminants, D(A) D(A 1 ) = D(1 n ) = 1 so D(A) 0. On the other hand, suppose D(A) 0. Let e i be the i th standard basis element of k n, as a column vector. For each j = 1,..., n Cramer s Rule gives us a solution b j to the equation Let B be the matrix whose j th column is b j. Then A b j = e j AB = 1 n To prove that also BA = 1 n we proceed a little indirectly. Let T M be the endomorphism of k n given by a matrix M. Then T A T B = T AB = T 1n = id k n Thus, T A is surjective. Since dim ImT A + dim ker T A = n necessarily T A is also injective, so is an isomorphism of k n. In particular, a right inverse is a left inverse, so also T BA = T B T A = id k n The only matrix that gives the identity map on k n is 1 n, so BA = 1 n. Thus, A is invertible. /// [3.0.4] Remark: All the above discussion assumes existence of erminants. 4. Existence The standard ad hoc argument for existence is ugly, and we won t write it out. If one must a way to proceed is to check directly by induction on size that an expansion by minors along any row or column meets the requirements for a erminant function. Then invoke uniqueness. This argument might be considered acceptable, but, in fact, it is much less illuminating than the use above of the key idea of multilinearity to prove properties of erminants before we re sure they exist. With hindsight, the capacity to talk about a erminant function D(A) which is linear as a function of each column (and is alternating) is very effective in proving properties of erminants. That is, without the notion of linearity a derivation of properties of erminants is much clumsier. This is why high-school treatments (and 200-year-old treatments) are awkward. By contrast, we need a more sophisticated viewpoint than basic linear algebra in order to give a conceptual reason for the existence of erminants. Rather than muddle through expansion by minors, we will wait until we have developed the exterior algebra that makes this straightforward. Exercises

9 Garrett: Abstract Algebra [4.0.1] Prove the expansion by minors formula for erminants, namely, for an n-by-n matrix A with entries a ij, letting A ij be the matrix obtained by deleting the i th row and j th column, for any fixed row index i, A = ( 1) i n j=1 ( 1) j a ij A ij and symmetrically for expansion along a column. (Hint: Prove that this formula is linear in each row/column, and invoke the uniqueness of erminants.) 26.[4.0.2] From just the most basic properties of erminants of matrices, show that the erminant of an upper-triangular matrix is the product of its diagonal entries. That is, show that a 11 a 12 a a 1n 0 a 22 a a 2n 0 0 a = a 11a 22 a a nn a nn 26.[4.0.3] Show that erminants respect block decompositions, at least to the extent that A B 0 D where A is an m-by-n matrix, B is m-by-n, and D is n-by-n. = A D 26.[4.0.4] By an example, show that it is not always the case that for blocks A, B, C, D. A B = A D B C C D 26.[4.0.5] Let x 1,..., x n and y 1,..., y n be two orthonormal bases in a real inner-product space. Let M be the matrix whose ij th entry is M ij = x i, y j Show that M = [4.0.6] For real numbers a, b, c, d, prove that a b = (area of parallelogram spanned by (a, b) and (c, d)) c d 26.[4.0.7] For real vectors v i = (x i, y i, z i ) with i = 1, 2, 3, show that x 1 y 1 z 1 x 2 y 2 z 2 = (volume of parallelogram spanned by v 1, v 2, v 3 ) x 3 y 3 z 3

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